\input texinfo
@c %**start of header
@setfilename R-FAQ.info
@settitle R FAQ
@setchapternewpage on
@set FAQ-YEAR 2000
@set FAQ-VERSION 1.1-8, 2000-08-14
@set REL-VERSION 1.1.1
@set DEV-VERSION 1.2.0
@c %**end of header

@dircategory Programming
@direntry
* R FAQ: (R-FAQ).               The R statistical system FAQ.
@end direntry

@finalout

@macro SPLUS{}
@sc{S-Plus}
@end macro

@macro HTML{}
@sc{html}
@end macro

@macro FORTRAN{}
@sc{Fortran}
@end macro

@titlepage
@title R FAQ
@subtitle Frequently Asked Questions on R
@subtitle Version @value{FAQ-VERSION}
@author Kurt Hornik
@end titlepage

@ifinfo
R FAQ                            @*
Frequently Asked Questions on R  @*
Version @value{FAQ-VERSION}      @*
Kurt Hornik                      @*

@sp 2
@end ifinfo

@ifhtml
@html
<html>
<head>
<TITLE>R FAQ</TITLE>
</head>

<body>
<h1>R FAQ</h1>
<h2>Frequently Asked Questions on R</h2>
<h2>Version @value{FAQ-VERSION}</h2>
<address>Kurt Hornik</address>
<p><p><hr><p>
@end html
@end ifhtml

@contents

@ifinfo
@sp 2
@end ifinfo

@node Top, Introduction, (dir), (dir)

@menu
* Introduction::                
* R Basics::                    
* R and S::                     
* R Web Interfaces::            
* R Add-On Packages::           
* R and Emacs::                 
* R Miscellania::               
* R Programming::               
* R Bugs::                      
* Acknowledgments::             
@end menu

@node Introduction, R Basics, Top, Top
@chapter Introduction

This document contains answers to some of the most frequently asked
questions about R.

@menu
* Legalese::                    
* Obtaining this document::     
* Citing this document::        
* Notation::                    
* Feedback::                    
@end menu

@node Legalese, Obtaining this document, Introduction, Introduction
@section Legalese

This document is free software; you can redistribute it and/or modify it
under the terms of the GNU General Public License as published by the
Free Software Foundation; either version 2, or (at your option) any
later version.

This document is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
General Public License for more details.

A copy of the GNU General Public License is available via WWW at

@display
@uref{http://www.gnu.org/copyleft/gpl.html}.
@end display

@noindent
You can also obtain it by writing to the Free Software Foundation, Inc.,
59 Temple Place --- Suite 330, Boston, MA 02111-1307, USA.

@node Obtaining this document, Citing this document, Legalese, Introduction
@section Obtaining this document

The latest version of this document is always available from

@display
@uref{http://www.ci.tuwien.ac.at/~hornik/R/}
@end display

From there, you can obtain versions converted to
@uref{http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.txt,, plain ASCII
text}, @uref{http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.dvi.gz,, DVI},
@uref{http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.info.gz,, GNU info},
@uref{http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html,, @HTML{}},
@uref{http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.pdf,, PDF},
@uref{http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.ps.gz,, PostScript} as
well as the @uref{http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.texi,,
Texinfo source} used for creating all these formats using the
@uref{http://texinfo.org/, GNU Texinfo system}.

You can also obtain the R FAQ from the @file{doc/FAQ} subdirectory of a
CRAN site (@ref{What is CRAN?}).

@node Citing this document, Notation, Obtaining this document, Introduction
@section Citing this document

In publications, please refer to this FAQ as Hornik (@value{FAQ-YEAR}),
``The R FAQ'' and give the above, @emph{official} URL.

@node Notation, Feedback, Citing this document, Introduction
@section Notation

Everything should be pretty standard.  @samp{R>} is used for the R
prompt, and a @samp{$} for the shell prompt (where applicable).

@node Feedback,  , Notation, Introduction
@section Feedback

Feedback is of course most welcome.

In particular, note that I do not have access to Windows or Mac systems.
Features specific to the Windows port of R are described in the
@uref{http://www.stats.ox.ac.uk/pub/R/rw-FAQ.html, ``Frequently Asked
Questions for R for Windows''}.  If you have information on Windows or
Mac systems that you think should be added to this document, please let
me know.

@c FIXME
@c Should we maybe have direct links inside the R tree to the various
@c rw-FAQ versions?

@node R Basics, R and S, Introduction, Top
@chapter R Basics

@menu
* What is R?::                  
* What machines does R run on?::  
* What is the current version of R?::  
* How can R be obtained?::      
* How can R be installed?::     
* Are there Unix binaries for R?::  
* What documentation exists for R?::  
* Citing R::                    
* What mailing lists exist for R?::  
* What is CRAN?::               
@end menu

@node What is R?, What machines does R run on?, R Basics, R Basics
@section What is R?

R is a system for statistical computation and graphics.  It consists of
a language plus a run-time environment with graphics, a debugger, access
to certain system functions, and the ability to run programs stored in
script files.

The design of R has been heavily influenced by two existing languages:
Becker, Chambers & Wilks' S (@pxref{What is S?}) and Sussman's
@uref{http://www.cs.indiana.edu/scheme-repository/home.html, Scheme}.
Whereas the resulting language is very similar in appearance to S, the
underlying implementation and semantics are derived from Scheme.
@xref{What are the differences between R and S?}, for further details.

The core of R is an interpreted computer language which allows branching
and looping as well as modular programming using functions.  Most of the
user-visible functions in R are written in R.  It is possible for the
user to interface to procedures written in the C, C++, or Fortran
languages for efficiency.  The R distribution contains functionality for
a large number of statistical procedures.  Among these are: linear and
generalized linear models, nonlinear regression models, time series
analysis, classical parametric and nonparametric tests, clustering and
smoothing.  There is also a large set of functions which provide a
flexible graphical environment for creating various kinds of data
presentations.  Additional modules (``add-on packages'') are available
for a variety of specific purposes (@pxref{R Add-On Packages}).

R was initially written by @email{Ross.Ihaka@@r-project.org, Ross Ihaka}
and @email{Robert.Gentleman@@r-project.org, Robert Gentleman}, who are
Senior Lecturers at the Department of Statistics of the University of
Auckland in Auckland, New Zealand.  In addition, a large group of
individuals has contributed to R by sending code and bug reports.

Since mid-1997 there has been a core group (the ``R Core Team'') who can
modify the R source code CVS archive.  The group currently consists of
Doug Bates, John Chambers, Peter Dalgaard, Robert Gentleman, Kurt
Hornik, Ross Ihaka, Friedrich Leisch, Thomas Lumley, Martin Maechler,
Guido Masarotto, Paul Murrell, Brian Ripley, Duncan Temple Lang, and
Luke Tierney.

R has a home page at @uref{http://www.r-project.org/}.  It is free
software distributed under a GNU-style copyleft, and an official part of
the GNU project (``GNU S'').

@node What machines does R run on?, What is the current version of R?, What is R?, R Basics
@section What machines does R run on?

R is being developed for the Unix, Windows and Mac families of operating
systems.

The current version of R will configure and build under a number of
common Unix platforms including i386-freebsd, i386-linux, ppc-linux,
mips-sgi-irix, alpha-linux, alpha-dec-osf4, rs6000-ibm-aix,
hppa-hp-hpux, sparc-linux, and sparc-sun-solaris.

@c and according to @email{jlindsey@@luc.ac.be, Jim Lindsey} also on
@c Mac, Amiga and Atari under m68k-linux.

If you know about other platforms, please drop us a note.

@node What is the current version of R?, How can R be obtained?, What machines does R run on?, R Basics
@section What is the current version of R?
 
The current stable Unix/Windows version is @value{REL-VERSION}, the
unstable one is @value{DEV-VERSION}.  Typically, new features are
introduced in the development versions; updates of stable versions are
for bug fixes mostly.  The version for the Mac is pre-alpha.

@node How can R be obtained?, How can R be installed?, What is the current version of R?, R Basics
@section How can R be obtained?

Sources, binaries and documentation for R can be obtained via CRAN, the
``Comprehensive R Archive Network'' (see @ref{What is CRAN?}).

Sources are also available via anonymous rsync.  Use

@example
rsync -rC rsync.r-project.org::@var{module} R
@end example

@noindent
to create a copy of the source tree specified by @var{module} in the
subdirectory @file{R} of the current directory, where @var{module}
specifies one of the three existing flavors of the R sources, and can be
one of @samp{r-release} (latest released version),
@samp{r-release-patched} (latest released version with patches applied),
and @samp{r-devel} (current development version).  The rsync trees are
created directly from the master CVS archive and are updated hourly.
The @option{-C} option in the rsync command is to cause it to skip the
CVS directories.  Further information on rsync is available at
@uref{http://rsync.samba.org/rsync/}.

@node How can R be installed?, Are there Unix binaries for R?, How can R be obtained?, R Basics
@section How can R be installed?

@menu
* How can R be installed (Unix)::  
* How can R be installed (Windows)::  
* How can R be installed (Macintosh)::  
@end menu

@node How can R be installed (Unix), How can R be installed (Windows), How can R be installed?, How can R be installed?
@subsection How can R be installed (Unix)

If binaries are available for your platform (see @ref{Are there Unix
binaries for R?}), you can use these, following the instructions that
come with them.

Otherwise, you can compile and install R yourself, which can be done
very easily under a number of common Unix platforms (see @ref{What
machines does R run on?}).  The file @file{INSTALL} that comes with the
R distribution contains instructions.

Note that you need a @FORTRAN{} compiler or @command{f2c} in addition to
a C compiler to build R.  Also, you need Perl version 5 to build the R
object documentations.  (If this is not available on your system, you
can obtain a PDF version of the object reference manual via CRAN.)

In the simplest case, untar the R source code, change to the directory
thus created, and issue the following commands (at the shell prompt):

@example
$ ./configure
$ make
@end example

If these commands execute successfully, the R binary and a shell script
font-end called @file{R} are created and copied to the @file{bin}
directory.  You can copy the script to a place where users can invoke
it, for example to @file{/usr/local/bin}.  In addition, plain text help
pages as well as @HTML{} and La@TeX{} versions of the documentation are
built.

Use @kbd{make dvi} to create DVI versions of the R manuals, such as
@file{refman.dvi} (an R object reference index) and @file{R-exts.dvi},
the ``R Extension Writers Guide'', in the @file{doc/manual}
subdirectory.  These files can be previewed and printed using standard
programs such as @command{xdvi} and @command{dvips}.  You can also use
@kbd{make pdf} to build PDF (Portable Document Format) version of the
manuals, and view these using Acrobat.  Manuals written in the GNU
Texinfo system can also be converted to info files suitable for reading
online with Emacs or stand-alone GNU Info; use @kbd{make info} to create
these versions (note that this requires @command{makeinfo} version 4).

Finally, use @kbd{make check} to find out whether your R system works
correctly.

You can also perform a ``system-wide'' installation using @kbd{make
install}.  By default, this will install to the following directories:

@table @file
@item $@{prefix@}/bin
the front-end shell script
@item $@{prefix@}/man/man1
the man page
@item $@{prefix@}/lib/R
all the rest (libraries, on-line help system, @dots{}).  This is the ``R
Home Directory'' (@env{R_HOME}) of the installed system.
@end table

@noindent
In the above, @code{prefix} is determined during configuration
(typically @file{/usr/local}) and can be set by running
@command{configure} with the option

@example
$ ./configure --prefix=/where/you/want/R/to/go
@end example

@noindent
(E.g., the R executable will then be installed into
@file{/where/you/want/R/to/go/bin}.)

To install DVI, info and PDF versions of the manuals, use @kbd{make
install-dvi}, @kbd{make install-info} and @kbd{make install-pdf},
respectively.

@node How can R be installed (Windows), How can R be installed (Macintosh), How can R be installed (Unix), How can R be installed?
@subsection How can R be installed (Windows)

The @file{bin/windows/windows-NT} directory of a CRAN site contains the
latest binary distributions of R for 32 bit versions of MS Windows
(i.e., 95, 98 or NT), as well as binary distributions for a large number
of add-on packages from CRAN.  The Windows version of R was created by
Robert Gentleman, and is now being developed and maintained by
@email{Guido.Masarotto@@r-project.org, Guido Masarotto} and
@email{Brian.Ripley@@r-project.org, Brian D. Ripley}.

@c Note that when uncompressing the zip files, the pkunzip program needs to
@c be invoked with the @samp{-D} flag to create subdirectories.  Also, be
@c aware that some decompression programs do not preserve long file names
@c properly.

For most installations the installer @command{rwinst.exe} will be the
easiest tool to use.

See the @uref{http://www.stats.ox.ac.uk/pub/R/rw-FAQ.html, R Windows
FAQ} for more details.

@node How can R be installed (Macintosh),  , How can R be installed (Windows), How can R be installed?
@subsection How can R be installed (Macintosh)

The Power Macintosh port is temporarily on hold, and currently no binary
distribution is available.  We hope that this will change soon.

@c The CRAN @file{bin/macos} directory contains @file{R.sea.hqx}, a
@c binhexed self-extracting archive, and installation instructions in
@c @file{README.MACINTOSH}.  Note that the version in it is nowhere near
@c the quality of the current Unix/Windows version.

@node Are there Unix binaries for R?, What documentation exists for R?, How can R be installed?, R Basics
@section Are there Unix binaries for R?

@c Linux binaries as of 2000-07-09:
@c
@c debian   2.2 i386  1.1.0  Doug Bates
@c          2.3 i386  1.1.0  Doug Bates
@c linuxppc 5.0 ppc   1.1.0  Alex Buerkle <buerkle@hawaii.edu>
@c redhat   6.x alpha 1.1.0  Naoki Takebayashi <ntakebay@bio.indiana.edu>
@c              i386  1.1.0  Martyn Plummer <plummer@iarc.fr>
@c              sparc 1.1.0  Vin Everett <vin.everett@mrc-bsu.cam.ac.uk>
@c suse     5.3 i386  1.0.1  Albrecht Gebhardt <albrecht.gebhardt@uni-klu.ac.at>
@c          6.4 i386  1.1.0  Albrecht Gebhardt <albrecht.gebhardt@uni-klu.ac.at>

The @file{bin/linux} directory of a CRAN site contains Debian 2.2/2.3
packages for the i386 platform (now part of the Debian distribution and
maintained by Doug Bates), Red Hat 6.x packages for the alpha, i386 and
sparc platforms (maintained by Naoki Takebayashi, Martyn Plummer, and
Vin Everett, respectively), SuSE 5.3/6.4 i386 packages by Albrecht
Gebhardt, and Linuxppc 5.0 RPMs by Alex Buerkle.

The @file{bin/osf} directory of a CRAN site contains RPMs by Albrecht
Gebhardt for alpha systems running Digital Unix 4.0.

@c There are also `tar' distributions for NEXTSTEP on the i386 and m68k
@c platforms in @file{bin/nextstep/i386} and @file{bin/nextstep/m68k}.

No other binary distributions have thus far been made publically
available.

@node What documentation exists for R?, Citing R, Are there Unix binaries for R?, R Basics
@section What documentation exists for R?

Online documentation for most of the functions and variables in R
exists, and can be printed on-screen by typing @kbd{help(@var{name})}
(or @kbd{?@var{name}}) at the R prompt, where @var{name} is the name of
the topic help is sought for.  (In the case of unary and binary
operators and control-flow special forms, the name may need to be be
quoted.)

This documentation can also be made available as one reference manual
for on-line reading in @HTML{} and PDF formats, and as hardcopy via
La@TeX{}, see @ref{How can R be installed?}.  An up-to-date @HTML{}
version is always available for web browsing at
@uref{http://stat.ethz.ch/R/manual/}.

The R distribution also comes with the following manuals.

@itemize @bullet
@item ``An Introduction to R'' (@file{R-intro})
includes information on data types, programming elements, statistical
modeling and graphics.  This document is based on the ``Notes on
@SPLUS{}'' by Bill Venables and David Smith.
@item ``Writing R Extensions'' (@file{R-exts})
currently describes the process of creating R add-on packages, writing R
documentation, R's system and foreign language interfaces, and the R
API.
@end itemize

@noindent
Furthermore, the ``R Language Definition'' manual (@file{R-lang}) is
currently being written, and will be available in R version 1.2.
@c FIXME 1.2
This is the ``Kernighan & Ritchie of R'', explaining evaluation,
parsing, object oriented programming, computing on the language, and so
forth.

In addition to material written specifically for R, documentation for
S/@SPLUS{} (see @ref{R and S}) can be used in combination with this FAQ
(@pxref{What are the differences between R and S?}).  We recommend

@quotation
W. N. Venables and B. D. Ripley (1999), ``Modern Applied Statistics with
@SPLUS{}.  Third Edition''.  Springer, ISBN 0-387-98825-4.
@end quotation

@noindent
This has a home page at @uref{http://www.stats.ox.ac.uk/pub/MASS3/}
providing additional material, in particular ``R Complements'' which
describe how to use the book with R.  These complements contain both
descriptions of some of the differences between R and @SPLUS{}, and the
modifications needed to run the examples in the book.  Its companion is

@quotation
W. N. Venables and B. D. Ripley (2000), ``S Programming''.  Springer,
ISBN 0-387-98966-8.
@end quotation

@noindent
This provides an in-depth guide to writing software in the S language
which forms the basis of both the commercial @SPLUS{} and the Open
Source R data analysis software systems.  See
@uref{http://www.stats.ox.ac.uk/pub/MASS3/Sprog/} for more information.

More introductory books are

@quotation
P. Spector (1994), ``An introduction to S and @SPLUS{}'', Duxbury Press.

A. Krause and M. Olsen (1997), ``The Basics of S and @SPLUS{}'',
Springer.
@end quotation

The book
@quotation
J. C. Pinheiro and D. M. Bates (2000), ``Mixed-Effects Models in S and
@SPLUS{}'', Springer, ISBN 0-387-98957-0
@end quotation

@noindent
provides a comprehensive guide to the use of the @strong{nlme} package
for linear and nonlinear mixed-effects models.  This has a home page at
@uref{http://nlme.stat.wisc.edu/MEMSS/}.

Last, but not least, Ross' and Robert's experience in designing and
implementing R is described in:

@smallexample
@group
@@article@{,
  author =    @{Ross Ihaka and Robert Gentleman@},
  title =     @{R: A Language for Data Analysis and Graphics@},
  journal =   @{Journal of Computational and Graphical Statistics@},
  year =      1996,
  volume =    5,
  number =    3,
  pages =     @{299--314@}
@}
@end group
@end smallexample

@node Citing R, What mailing lists exist for R?, What documentation exists for R?, R Basics
@section Citing R

To cite R in publications, use the above Ihaka & Gentleman (1996), ``R:
A Language for Data Analysis and Graphics'', @emph{Journal of
Computational and Graphical Statistics}, @strong{5}, 299--314.

@node What mailing lists exist for R?, What is CRAN?, Citing R, R Basics
@section What mailing lists exist for R?

Thanks to @email{Martin.Maechler@@r-project.org, Martin Maechler}, there
are three mailing lists devoted to R.

@table @code
@item r-announce
This list is for announcements about the development of R and the
availability of new code.
@item r-devel
This list is for discussions about the future of R and pre-testing of
new versions.  It is meant for those who maintain an active position in
the development of R.
@item r-help
The `main' R mailing list, for announcements about the development of R
and the availability of new code, questions and answers about problems
and solutions using R, enhancements and patches to the source code and
documentation of R, comparison and compatibility with S and @SPLUS{},
and for the posting of nice examples and benchmarks.
@end table

@noindent
Note that the r-announce list is gatewayed into r-help, so you don't
need to subscribe to both of them.

Send email to @email{r-help@@lists.r-project.org} to reach everyone on
the r-help mailing list.  To subscribe (or unsubscribe) to this list
send @samp{subscribe} (or @samp{unsubscribe}) in the @emph{body} of the
message (not in the subject!) to
@email{r-help-request@@lists.r-project.org}.  Information about the list
can be obtained by sending an email with @samp{info} as its contents to
@email{r-help-request@@lists.r-project.org}.

Subscription and posting to the other lists is done analogously, with
`r-help' replaced by `r-announce' and `r-devel', respectively.

It is recommended that you send mail to r-help rather than only to the R
developers (who are also subscribed to the list, of course).  This may
save them precious time they can use for constantly improving R, and
will typically also result in much quicker feedback for yourself.

Of course, in the case of bug reports it would be very helpful to have
code which reliably reproduces the problem.  Also, make sure that you
include information on the system and version of R being used.  See
@ref{R Bugs} for more details.

Archives of the above three mailing lists are made available on the net
in a monthly schedule via the @file{doc/html/mail.html} file in CRAN.
Searchable archives of the lists are available via
@uref{http://www.ens.gu.edu.au/robertk/R/}.

The R Core Team can be reached at @email{r-core@@lists.r-project.org}
for comments and reports.

@node What is CRAN?,  , What mailing lists exist for R?, R Basics
@section What is CRAN?

The ``Comprehensive R Archive Network'' (CRAN) is a collection of sites
which carry identical material, consisting of the R distribution(s), the
contributed extensions, documentation for R, and binaries.

The CRAN master site can be found at the URL

@quotation
@multitable @columnfractions .45 .30
@item @uref{http://cran.r-project.org/}
@tab (Austria)
@end multitable
@end quotation

@noindent
(which is the same as @uref{http://cran.at.r-project.org/}) and is
currently being mirrored daily at

@quotation
@multitable @columnfractions .45 .30
@item @uref{http://cran.dk.r-project.org/}
@c @uref{http://SunSITE.auc.dk/R/}
@tab (Denmark)
@item @uref{http://cran.hu.r-project.org/}
@tab (Hungary)
@item @uref{http://cran.it.r-project.org/}
@c @uref{http://www.stat.unipg.it/pub/stat/statlib/R/CRAN/}
@tab (Italy)
@item @uref{http://cran.ch.r-project.org/}
@c @uref{http://stat.ethz.ch/CRAN/}
@tab (Switzerland)
@item @uref{http://cran.uk.r-project.org/}
@c @uref{http://www.stats.bris.ac.uk/R/}
@tab (United Kingdom)
@item @uref{http://cran.us.r-project.org/}
@c @uref{http://cran.stat.wisc.edu/}
@tab (USA/Wisconsin)
@c @item @uref{http://mirror.aarnet.edu.au/CRAN/}
@c @tab (Australia)
@c @item @uref{ftp://ftp.u-aizu.ac.jp/pub/lang/R/CRAN/}
@c @tab (Japan)
@c @item @uref{ftp://dola.snu.ac.kr/pub/R/CRAN/}
@c @tab (South Korea)
@c @item @uref{http://lib.stat.cmu.edu/R/CRAN/}
@c @tab (USA/Pennsylvania)
@c @item @uref{ftp://ftp.biostat.washington.edu/mirrors/R/CRAN/}
@c @tab (USA/Washington)
@end multitable
@end quotation

@noindent
Please use the CRAN site closest to you to reduce network load.

From CRAN, you can obtain the latest official release of R, daily
snapshots of R (copies of the current CVS trees), as gzipped and bzipped
tar files or as two gzipped tar files (ready for 1.4M floppies), a
wealth of additional contributed code, as well as prebuilt binaries for
various operating systems (Linux, Digital Unix, and MS Windows).  CRAN
also provides access to documentation on R, existing mailing lists and
the R Bug Tracking system.

To ``submit'' to CRAN, simply upload to
@uref{ftp://cran.r-project.org/incoming/} and send an email to
@email{wwwadmin@@cran.r-project.org}.

@quotation
@strong{Note:}  It is very important that you indicate the copyright
(license) information (GPL, BSD, Artistic, @dots{}) in your submission.
@end quotation

Please always use the URL of the master site when referring to CRAN.

@node R and S, R Web Interfaces, R Basics, Top
@chapter R and S

@menu
* What is S?::                  
* What is S-PLUS?::             
* What are the differences between R and S?::  
* Is there anything R can do that S-PLUS cannot?::  
@end menu

@node What is S?, What is S-PLUS?, R and S, R and S
@section What is S?

S is a very high level language and an environment for data analysis and
graphics.  In 1998, the Association for Computing Machinery (ACM)
presented its Software System Award to John M. Chambers, the principal
designer of S, for

@quotation
the S system, which has forever altered the way people analyze,
visualize, and manipulate data @dots{}

S is an elegant, widely accepted, and enduring software system, with
conceptual integrity, thanks to the insight, taste, and effort of John
Chambers.
@end quotation

The evolution of the S language is characterized by four books by John
Chambers and coauthors, which are also the primary references for S.

@itemize @bullet
@item
Richard A. Becker and John M. Chambers (1984), ``S.  An Interactive
Environment for Data Analysis and Graphics,'' Monterey: Wadsworth and
Brooks/Cole.

This is also referred to as the ``@emph{Brown Book}'', and of historical
interest only.

@item
Richard A. Becker, John M. Chambers and Allan R. Wilks (1988), ``The New
S Language,'' London: Chapman & Hall.

This book is often called the ``@emph{Blue Book}'', and introduced what
is now known as S version 2.

@item
John M. Chambers and Trevor J. Hastie (1992), ``Statistical Models in
S,''  London: Chapman & Hall.

This is also called the ``@emph{White Book}'', and introduced S version
3, which added structures to facilitate statistical modeling in S.

@item
John M. Chambers (1998), ``Programming with Data,'' New York: Springer,
ISBN 0-387-98503-4
(@url{http://cm.bell-labs.com/cm/ms/departments/sia/Sbook/}).

This ``@emph{Green Book}'' describes version 4 of S, a major revision of
S designed by John Chambers to improve its usefulness at every stage of
the programming process.
@end itemize

See @uref{http://cm.bell-labs.com/cm/ms/departments/sia/S/history.html}
for further information on ``Stages in the Evolution of S''.

There is a huge amount of user-contributed code for S, available at the
@uref{http://lib.stat.cmu.edu/S/, S Repository} at CMU.

@c The @uref{http://lib.stat.cmu.edu/S/faq, ``Frequently Asked Questions
@c about S''} contains further information about S, but is not
@c up-to-date.

@node What is S-PLUS?, What are the differences between R and S?, What is S?, R and S
@section What is @sc{S-Plus}?

@SPLUS{} is a value-added version of S sold by Mathsoft, Inc.  Based on
the S language, @SPLUS{} provides functionality in a wide variety of
areas, including robust regression, modern non-parametric regression,
time series, survival analysis, multivariate analysis, classical
statistical tests, quality control, and graphics drivers.  Add-on
modules add additional capabilities for wavelet analysis, spatial
statistics, GARCH models, and design of experiments.

See the @uref{http://www.mathsoft.com/splus/, MathSoft @SPLUS{} page}
for further information.

@node What are the differences between R and S?, Is there anything R can do that S-PLUS cannot?, What is S-PLUS?, R and S
@section What are the differences between R and S?

We can regard S as a language with three current implementations or
``engines'', the ``old S engine'' (S version 3; @SPLUS{} 3.x and 4.x),
the ``new S engine'' (S version 4; @SPLUS{} 5.x and above), and R.
Given this understanding, asking for ``the differences between R and S''
really amounts to asking for the specifics of the R implementation of
the S language, i.e., the difference between the R and S engines.

For the remainder of this section, ``S'' refers to the S engines and not
the S language.

@menu
* Lexical scoping::             
* Models::                      
* Others::                      
@end menu

@node Lexical scoping, Models, What are the differences between R and S?, What are the differences between R and S?
@subsection Lexical scoping

Contrary to other implementations of the S language, R has adopted the
evaluation model of Scheme.

This difference becomes manifest when @emph{free} variables occur in a
function.  Free variables are those which are neither formal parameters
(occurring in the argument list of the function) nor local variables
(created by assigning to them in the body of the function).  Whereas S
(like C) by default uses @emph{static} scoping, R (like Scheme) has
adopted @emph{lexical} scoping.  This means the values of free variables
are determined by a set of global variables in S, but in R by the
bindings that were in effect at the time the function was created.

Consider the following function:

@example
cube <- function(n) @{
  sq <- function() n * n
  n * sq()
@}
@end example

Under S, @code{sq()} does not ``know'' about the variable @code{n}
unless it is defined globally:

@example
S> cube(2)
Error in sq():  Object "n" not found
Dumped
S> n <- 3
S> cube(2)
[1] 18
@end example

In R, the ``environment'' created when @code{cube()} was invoked is
also looked in:

@example
R> cube(2)
[1] 8
@end example

@c The following more `realistic' example illustrating the differences in
@c scoping is due to @email{thomas@@biostat.washington.edu, Thomas Lumley}.
@c The function

@c @example
@c jackknife.lm <- function(lmobj) @{
@c   n <- length(resid(lmobj))
@c   jval <- sapply(1:n, function(i) coef(update(lmobj, subset = -i)))
@c   (n - 1) * (n - 1) * var(jval) / n
@c @}
@c @end example

@c @noindent
@c does something useful in R, but does not work in S.  In order to make it
@c work in S you need to explicitly pass the linear model object into the
@c function nested in @code{apply()}.  If you don't and you are lucky you
@c will get @samp{Error: Object "lmobj" not found}.  If you are unlucky
@c enough to have a linear model called @code{lmobj} in your global
@c environment you will get the wrong answer with no warning.

@c The following version works in S.

@c @example
@c jackknife.S.lm <- function(lmobj) @{
@c   n <- length(resid(lmobj))
@c   jval <- sapply(1:n,
@c                  function(i, lmobj) coef(update(lmobj, subset = -i)), 
@c                  lmobj = lmobj)
@c   (n - 1) * (n - 1) * var(jval) / n
@c @}
@c @end example

@c (The S version was written independently by Thomas and at least three of
@c his fellow students over the past couple of years, causing literally
@c hours of confusion on each occasion.)

As a more ``interesting'' real-world problem, suppose you want to write
a function which returns the density function of the @math{r}-th order
statistic from a sample of size @math{n} from a (continuous)
distribution.  For simplicity, we shall use both the cdf and pdf of the
distribution as explicit arguments.  (Example compiled from various
postings by Luke Tierney.)

The @SPLUS{} documentation for @code{call()} basically suggests the
following:

@example
dorder <- function(n, r, pfun, dfun) @{
  f <- function(x) NULL
  con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1)))
  PF <- call(substitute(pfun), as.name("x"))
  DF <- call(substitute(dfun), as.name("x"))
  f[[length(f)]] <-
    call("*", con,
         call("*", call("^", PF, r - 1),
              call("*", call("^", call("-", 1, PF), n - r),
                   DF)))
  f
@}
@end example

@noindent Rather tricky, isn't it?  The code uses the fact that in S,
functions are just lists of special mode with the function body as the
last argument, and hence does not work in R (one could make the idea
work, though).

A version which makes heavy use of @code{substitute()} and seems to work
under both S and R is

@example
dorder <- function(n, r, pfun, dfun) @{
  con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1)))
  eval(substitute(function(x) K * PF(x)^a * (1 - PF(x))^b * DF(x),
                  list(PF = substitute(pfun), DF = substitute(dfun),
                       a = r - 1, b = n - r, K = con)))
@}
@end example

@noindent
(the @code{eval()} is not needed in S).

However, in R there is a much easier solution:

@example
dorder <- function(n, r, pfun, dfun) @{
  con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1)))
  function(x) @{
    con * pfun(x)^(r - 1) * (1 - pfun(x))^(n - r) * dfun(x)
  @}
@}
@end example

@noindent
This seems to be the ``natural'' implementation, and it works because
the free variables in the returned function can be looked up in the
defining environment (this is lexical scope).

Note that what you really need is the function @emph{closure}, i.e., the
body along with all variable bindings needed for evaluating it.  Since
in the above version, the free variables in the value function are not
modified, you can actually use it in S as well if you abstract out the
closure operation into a function @code{MC()} (for ``make closure''):

@example
dorder <- function(n, r, pfun, dfun) @{
  con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1)))
  MC(function(x) @{
       con * pfun(x)^(r - 1) * (1 - pfun(x))^(n - r) * dfun(x)
     @},
     list(con = con, pfun = pfun, dfun = dfun, r = r, n = n))
@}
@end example

Given the appropriate definitions of the closure operator, this works in
both R and S, and is much ``cleaner'' than a substitute/eval solution
(or one which overrules the default scoping rules by using explicit
access to evaluation frames, as is of course possible in both R and S).

For R, @code{MC()} simply is

@example
MC <- function(f, env) f
@end example

@noindent (lexical scope!), a version for S is

@example
MC <- function(f, env = NULL) @{
  env <- as.list(env)
  if (mode(f) != "function")
    stop(paste("not a function:", f))
  if (length(env) > 0 && any(names(env) == ""))
    stop(paste("not all arguments are named:", env))
  fargs <- if(length(f) > 1) f[1:(length(f) - 1)] else NULL
  fargs <- c(fargs, env)
  if (any(duplicated(names(fargs))))
    stop(paste("duplicated arguments:", paste(names(fargs)),
         collapse = ", "))
  fbody <- f[length(f)]
  cf <- c(fargs, fbody)
  mode(cf) <- "function"
  return(cf)
@}
@end example

Similarly, most optimization (or zero-finding) routines need some
arguments to be optimized over and have other parameters that depend on
the data but are fixed with respect to optimization.  With R scoping
rules, this is a trivial problem; simply make up the function with the
required definitions in the same environment and scoping takes care of
it.  With S, one solution is to add an extra parameter to the function
and to the optimizer to pass in these extras, which however can only
work if the optimizer supports this.

Lexical scoping allows using function closures and maintaining local
state.  A simple example (taken from Abelson and Sussman) is obtained by
typing @kbd{demo(scoping)} at the R prompt.  Further information is
provided in the standard R reference ``R: A Language for Data Analysis
and Graphics'' (@pxref{What documentation exists for R?}) and a paper on
``Lexical Scope and Statistical Computing'' by Robert Gentleman and Ross
Ihaka which can be obtained from the @file{doc/misc} directory of a CRAN
site and will appear in the
@uref{http://www.amstat.org/publications/jcgs/, , @emph{Journal of
Computational and Graphical Statistics}} around the beginning of 2000.

@c CHECK JCGS paper ``Lexical Scope and Statistical Computing''

Lexical scoping also implies a further major difference.  Whereas S
stores all objects as separate files in a directory somewhere (usually
@file{.Data} under the current directory), R does not.  All objects
in R are stored internally.  When R is started up it grabs a very large
piece of memory and uses it to store the objects.  R performs its own
memory management of this piece of memory.  Having everything in memory
is necessary because it is not really possible to externally maintain
all relevant ``environments'' of symbol/value pairs.  This difference
also seems to make R @emph{faster} than S.

The down side is that if R crashes you will lose all the work for the
current session.  Saving and restoring the memory ``images'' (the
functions and data stored in R's internal memory at any time) can be a
bit slow, especially if they are big.  In S this does not happen,
because everything is saved in disk files and if you crash nothing is
likely to happen to them.  (In fact, one might conjecture that the S
developers felt that the price of changing their approach to persistent
storage just to accommodate lexical scope was far too expensive.)
Hence, when doing important work, you might consider saving often (see
@ref{How can I save my workspace?}) to safeguard against possible
crashes.  Other possibilities are logging your sessions, or have your R
commands stored in text files which can be read in using
@code{source()}.

@quotation
@strong{Note:}  If you run R from within Emacs (see @ref{R and Emacs}),
you can save the contents of the interaction buffer to a file and
conveniently manipulate it using @code{ess-transcript-mode}, as well as
save source copies of all functions and data used.
@end quotation

@node Models, Others, Lexical scoping, What are the differences between R and S?
@subsection Models

There are some differences in the modeling code, such as

@itemize @bullet
@item
Whereas in S, you would use @code{lm(y ~ x^3)} to regress @code{y} on
@code{x^3}, in R, you have to insulate powers of numeric vectors (using
@code{I()}), i.e., you have to use @code{lm(y ~ I(x^3))}.
@item
The glm family objects are implemented differently in R and S.  The same
functionality is available but the components have different names.
@item
Option @code{na.action} is set to @code{"na.omit"} by default in R,
but not set in S.
@item
Terms objects are stored differently.  In S a terms object is an
expression with attributes, in R it is a formula with attributes.  The
attributes have the same names but are mostly stored differently.  The
major difference in functionality is that a terms object is
subscriptable in S but not in R.  If you can't imagine why this would
matter then you don't need to know.
@item
Finally, in R @code{y~x+0} is an alternative to @code{y~x-1} for
specifying a model with no intercept.  Models with no parameters at all
can be specified by @code{y~0}.
@end itemize

@node Others,  , Models, What are the differences between R and S?
@subsection  Others

Apart from lexical scoping and its implications, R follows the S
language definition in the Blue and White Books as much as possible, and
hence really is an ``implementation'' of S.  There are some intentional
differences where the behavior of S is considered ``not clean''.  In
general, the rationale is that R should help you detect programming
errors, while at the same time being as compatible as possible with S.

Some known differences are the following.

@itemize @bullet

@item
In R, if @code{x} is a list, then @code{x[i] <- NULL} and @code{x[[i]]
<- NULL} remove the specified elements from @code{x}.  The first of
these is incompatible with S, where it is a no-op.  (Note that you can
set elements to @code{NULL} using @code{x[i] <- list(NULL)}.)

@item
In S, the functions named @code{.First} and @code{.Last} in the
@file{.Data} directory can be used for customizing, as they are executed
at the very beginning and end of a session, respectively.

In R, the startup mechanism is as follows.  R first sources the system
startup file @file{@env{$@w{R_HOME}}/library/base/R/Rprofile}.  Then, it
searches for a site-wide startup profile unless the command line option
@option{--no-site-file} was given.  The name of this file is taken from
the value of the @env{R_PROFILE} environment variable.  If that variable
is unset, the default is @file{@env{$R_HOME}/etc/Rprofile}.  Then,
unless @option{--no-init-file} was given, R searches for a file called
@file{.Rprofile} in the current directory or in the user's home
directory (in that order) and sources it.  It also loads a saved image
from @file{.RData} in case there is one (unless @option{--no-restore}
was specified).  If needed, the functions @code{.First()} and
@code{.Last()} should be defined in the appropriate startup profiles.

@item
In R, @code{T} and @code{F} are just variables being set to @code{TRUE}
and @code{FALSE}, respectively, but are not reserved words as in S and
hence can be overwritten by the user.  (This helps e.g.@: when you have
factors with levels @code{"T"} or @code{"F"}.)  Hence, when writing code
you should always use @code{TRUE} and @code{FALSE}.

@item
In R, @code{dyn.load()} can only load @emph{shared libraries}, as
created for example by @kbd{R SHLIB}.

@item
In R, @code{attach()} currently only works for lists and data frames
(not for directories).  Also, you cannot attach at position 1.

@item
Categories do not exist in R, and never will as they are deprecated now
in S.  Use factors instead.

@item
In R, @code{For()} loops are not necessary and hence not supported.

@item
In R, @code{assign()} uses the argument @option{envir=} rather than
@option{where=} as in S.

@item
The random number generators are different, and the seeds have different
length.

@item
R passes integer objects to C as @code{int *} rather than @code{long *}
as in S.

@item
R has no single precision storage mode.  However, as of version 0.65.1,
there is a single precision interface to C/@FORTRAN{} subroutines.

@item
By default, @code{ls()} returns the names of the objects in the current
(under R) and global (under S) environment, respectively.  For example,
given

@smallexample
x <- 1; fun <- function() @{y <- 1; ls()@}
@end smallexample

@noindent
then @code{fun()} returns @code{"y"} in R and @code{"x"} (together with
the rest of the global environment) in S.

@item
R allows for zero-extent matrices (and arrays, i.e., some elements of
the @code{dim} attribute vector can be 0).  This has been determined a
useful feature as it helps reducing the need for special-case tests for
empty subsets.  For example, if @code{x} is a matrix, @code{x[, FALSE]}
is not @code{NULL} but a ``matrix'' with 0 columns.  Hence, such objects
need to be tested for by checking whether their @code{length()} is zero
(which works in both R and S), and not using @code{is.null()}.

@item
Named vectors are considered vectors in R but not in S (e.g.,
@code{is.vector(c(a = 1:3))} returns @code{FALSE} in S and @code{TRUE}
in R).

@item
Data frames are not considered as matrices in R (i.e., if @code{DF} is a
data frame, then @code{is.matrix(DF)} returns @code{FALSE} in R and
@code{TRUE} in S).

@item
R by default uses treatment contrasts in the unordered case, whereas S
uses the Helmert ones.  This is a deliberate difference reflecting the
opinion that treatment contrasts are more natural.

@item
In R, the last argument (which corresponds to the right hand side) of an
assignment function must be named @samp{value}.  E.g., @code{fun(a) <-
b} is evaluated as @code{(fun<-)(a, value = b)}.

@item
In S, @code{substitute()} searches for names for substitution in the
given expression in three places: the actual and the default arguments
of the matching call, and the local frame (in that order).  R looks in
the local frame only, with the special rule to use a ``promise'' if a
variable is not evaluated.  Since the local frame is initialized with
the actual arguments or the default expressions, this is usually
equivalent to S, until assignment takes place.

@item
In R, @code{eval(EXPR, sys.parent())} does not work.  Instead, one
should use @code{eval(EXPR, sys.frame(sys.parent())),} which also works
in S.

@item
In S, the index variable in a @code{for()} loop is local to the inside
of the loop.  In R it is local to the environment where the @code{for()}
statement is executed.

@item
In S, @code{tapply(simplify=TRUE)} returns a vector where R returns a
one-dimensional array (which can have named dimnames).

@end itemize

There are also differences which are not intentional, and result from
missing or incorrect code in R.  The developers would appreciate hearing
about any deficiencies you may find (in a written report fully
documenting the difference as you see it).  Of course, it would be
useful if you were to implement the change yourself and make sure it
works.

@node Is there anything R can do that S-PLUS cannot?,  , What are the differences between R and S?, R and S
@section Is there anything R can do that @sc{S-Plus} cannot?

Since almost anything you can do in R has source code that you could
port to @SPLUS{} with little effort there will never be much you can do
in R that you couldn't do in @SPLUS{} if you wanted to.  (Note that
using lexical scoping may simplify matters considerably, though.)

R offers several graphics features that @SPLUS{} does not, such as finer
handling of line types, more convenient color handling (via palettes),
gamma correction for color, and, most importantly, mathematical
annotation in plot texts, via input expressions reminiscent of @TeX{}
constructs.  See the help page for @code{plotmath}, which features an
impressive on-line example.  The paper ``An Approach to Providing
Mathematical Annotation in Plots'' by Paul Murrell and Ross Ihaka, which
will soon appear in the @uref{http://www.amstat.org/publications/jcgs/,
, @emph{Journal of Computational and Graphical Statistics}}, has more
details on this.

@c CHECK JCGS paper ``An Approach to Providing Mathematical Annotations
@c Plots''

@node R Web Interfaces, R Add-On Packages, R and S, Top
@chapter R Web Interfaces

@strong{Rcgi} is a CGI WWW interface to R by @email{h089@@mth.uea.ac.uk,
Mark J. Ray}.  Recent versions have the ability to use ``embedded
code'': you can mix user input and code, allowing the @HTML{} author to
do anything from load in data sets to enter most of the commands for
users without writing CGI scripts.  Graphical output is possible in
PostScript or GIF formats and the executed code is presented to the user
for revision.

Demo and download are available from
@uref{http://www.mth.uea.ac.uk/~h089/Rcgi/}.

@strong{Rweb} is developed and maintained by
@email{jeff@@math.montana.edu, Jeff Banfield}.  The
@uref{http://www.math.montana.edu/Rweb/, Rweb Home Page} provides access
to all three versions of Rweb---a simple text entry form that returns
output and graphs, a more sophisticated Javascript version that provides
a multiple window environment, and a set of point and click modules that
are useful for introductory statistics courses and require no knowledge
of the R language.  All of the Rweb versions can analyze Web accessible
datasets if a URL is provided.

The paper ``Rweb: Web-based Statistical Analysis'', providing a detailed
explanation of the different versions of Rweb and an overview of how
Rweb works, was published in the Journal of Statistical Software
(@uref{http://www.stat.ucla.edu/journals/jss/v04/i01/}).

@node R Add-On Packages, R and Emacs, R Web Interfaces, Top
@chapter R Add-On Packages

@menu
* Which add-on packages exist for R?::  
* How can add-on packages be installed?::  
* How can add-on packages be used?::  
* How can add-on packages be removed?::  
* How can I create an R package?::  
* How can I contribute to R?::  
@end menu

@node Which add-on packages exist for R?, How can add-on packages be installed?, R Add-On Packages, R Add-On Packages
@section Which add-on packages exist for R?

The R distribution comes with the following extra packages:

@table @strong
@item ctest
A collection of Classical TESTs, including the Bartlett, Fisher,
Kruskal-Wallis, Kolmogorov-Smirnov, and Wilcoxon tests.
@item eda
Exploratory Data Analysis.  Currently only contains functions for robust
line fitting, and median polish and smoothing.
@item lqs
Resistant regression and covariance estimation.
@item modreg
MODern REGression: smoothing and local methods.
@item mva
MultiVariate Analysis.  Currently contains code for principal
components, canonical correlations, metric multidimensional scaling, and
hierarchical and k-means clustering.
@item nls
Nonlinear regression routines.
@item splines
Regression spline functions and classes.
@item stepfun
Code for dealing with STEP FUNctions, including empirical cumulative
distribution functions.
@item tcltk
Interface and language bindings to Tcl/Tk GUI elements.
@item ts
Time Series.
@end table

The following packages are available from the CRAN @file{src/contrib}
area.

@table @strong
@item Devore5
Data sets and sample analyses from ``Probability and Statistics for
Engineering and the Sciences (5th ed)'' by Jay L. Devore, 2000, Duxbury.
@item GenKern
Functions for generating and manipulating generalised binned kernel
density estimates.
@item KernSmooth
Functions for kernel smoothing (and density estimation) corresponding to
the book ``Kernel Smoothing'' by M. P. Wand and M. C. Jones, 1995.
@item MASS
Functions and datasets from the main package of Venables and Ripley,
``Modern Applied Statistics with @SPLUS{}''.  Contained in the @file{VR}
bundle.
@item Matrix
A Matrix package.
@item NISTnls
A set of test nonlinear least squares examples from NIST, the
U.S. National Institute for Standards and Technology.
@item RODBC
ODBC support and a back end database.
@item RmSQL
An interface between R and the mSQL database system.
@c @item Rnotes
@c The data sets for the exercises in ``An Introduction to R''
@c (@pxref{What documentation exists for R?}).
@item SASmixed
Data sets and sample linear mixed effects analyses corresponding to the
examples in ``SAS System for Mixed Models'' by R. C. Littell,
G. A. Milliken, W. W. Stroup and R. D. Wolfinger, 1996, SAS Institute.
@item acepack
ace (Alternating Conditional Expectations) and avas (Additivity and
VAriance Stabilization for regression) for selecting regression
transformations.
@item akima
Linear or cubic spline interpolation for irregularly gridded data.
@item ash
Programs for 1D and 2D density estimation.
@item bindata
Generation of correlated artificial binary data.
@item boot
Functions and datasets for bootstrapping from the book ``Bootstrap
Methods and Their Applications'' by A. C. Davison and D. V. Hinkley,
1997, Cambridge University Press.
@item bootstrap
Software (bootstrap, cross-validation, jackknife), data and errata for
the book ``An Introduction to the Bootstrap'' by B. Efron and
R. Tibshirani, 1993, Chapman and Hall.
@item cclust
Convex clustering methods, including @math{k}-means algorithm, on-line
update algorithm (Hard Competitive Learning) and Neural Gas algorithm
(Soft Competitive Learning) and calculation of several indexes for
finding the number of clusters in a data set.
@item cfa
Analysis of configuration frequencies.
@item chron
A package for working with chronological objects (times and dates).
@item class
Functions for classification (@math{k}-nearest neighbor and LVQ).
Contained in the @file{VR} bundle.
@item cluster
Functions for cluster analysis.
@item coda
Output analysis and diagnostics for Markov Chain Monte Carlo (MCMC)
simulations.
@item date
Functions for dealing with dates.  The most useful of them accepts a
vector of input dates in any of the forms @samp{8/30/53},
@samp{30Aug53}, @samp{30 August 1953}, @dots{}, @samp{August 30 53}, or
any mixture of these.
@item e1071
Miscellaneous functions used at the Department of Statistics at TU Wien
(E1071), including moments, short-time Fourier transforms, Independent
Component Analysis, and simulation of a Wiener process.
@item fdim
Functions for calculating fractal dimension.
@item fracdiff
Maximum likelihood estimation of the parameters of a fractionally
differenced ARIMA(@math{p,d,q}) model (Haslett and Raftery, Applied
Statistics, 1989).
@item gee
An implementation of the Liang/Zeger generalized estimating equation
approach to GLMs for dependent data.
@item gss
A comprehensive package for structural multivariate function estimation
using smoothing splines.
@item hpower
A suite of functions to compute power and sample size for tests of the
general linear hypothesis.
@item ineq
Inequality, concentration and poverty measures, and Lorenz curves
(empirical and theoretic).
@item integrate
Adaptive quadrature in up to 20 dimensions.
@item leaps
A package which performs an exhaustive search for the best subsets of a
given set of potential regressors, using a branch-and-bound algorithm,
and also performs searches using a number of less time-consuming
techniques.
@item lmtest
A collection of tests on the assumptions of linear regression models
from the book ``The linear regression model under test'' by W. Kraemer
and H. Sonnberger, 1986, Physica.
@item locfit
Local Regression, likelihood and density estimation.
@item logspline
Logspline density estimation.
@item maptree
Functions with example data for graphing and mapping models from
hierarchical clustering and classification and regression trees.
@item mclust
Model-based cluster analysis.
@item mda
Code for mixture discriminant analysis (MDA), flexible discriminant
analysis (FDA), penalized discriminant analysis (PDA), multivariate
additive regression splines (MARS), adaptive back-fitting splines
(BRUTO), and penalized regression.
@item mlbench
A collection of artificial and real-world machine learning benchmark
problems, including the Boston housing data.
@item multilm
A basic method for fitting and testing multivariate linear models,
including stabilized test procedures by Laeuter et.@: al.
@item multiv
Functions for hierarchical clustering, partitioning, bond energy
algorithm, Sammon mapping, PCA and correspondence analysis.
@item nlme
Fit and compare Gaussian linear and nonlinear mixed-effects models.
@item nnet
Software for single hidden layer perceptrons (``feed-forward neural
networks''), and for multinomial log-linear models.  Contained in the
@file{VR} bundle.
@item norm
Analysis of multivariate normal datasets with missing values.
@item oz
Functions for plotting Australia's coastline and state boundaries.
@item pls
Univariate Partial Least Squares Regression.
@item polymars
Polychotomous regression based on Multivariate Adaptive Regression
Splines.
@item polynom
A collection of functions to implement a class for univariate polynomial
manipulations.
@item princurve
Fits a principal curve to a matrix of points in arbitrary dimension.
@item pspline
Smoothing splines with penalties on order @math{m} derivatives.
@item quadprog
For solving quadratic programming problems.
@item quantreg
Compute regression quantiles and some related rank statistics.
@c @item ratetables
@c US national and state mortality data (requires @strong{survival4} and
@c @strong{date}), for use with @strong{survival4}.
@item rmeta
Functions for simple fixed and random effects meta-analysis for
two-sample comparison of binary outcomes.
@item rpart
Recursive Partitioning.
@item sgeostat
An object-oriented framework for geostatistical modeling.
@item sm
Software linked to the book ``Applied Smoothing Techniques for Data
Analysis:  The Kernel Approach with @SPLUS{} Illustrations'' by
A. W. Bowman and A. Azzalini (1997), Oxford University Press.
@c @item snns
@c An R interface to the Stuttgart Neural Networks Simulator (SNNS).
@item spatial
Functions for kriging and point pattern analysis from ``Modern Applied
Statistics with @SPLUS{}'' by W. Venables and B. Ripley.  Contained in
the @file{VR} bundle.
@c @item stable
@c Density, distribution, quantile and hazard functions of a stable
@c variate; generalized linear models for the parameters of a stable
@c distribution.
@item stataread
Read and write Stata v6 @file{.dta} files.
@c @item survival4
@c Functions for survival analysis, version 4 (requires @strong{splines}).
@item survival5
Functions for survival analysis, version 5 (suggests @strong{date}), the
main new feature being penalized (partial) likelihood.
@item tree
Classification and regression trees.
@item tripack
A constrained two-dimensional Delaunay triangulation package.
@item tseries
Additional code for time series analysis.
@item wavethresh
Software to perform 1-d and 2-d wavelet statistics and transforms.
@item wle
Robust statistical inference via a weighted likelihood approach.
@item xgobi
Interface to the XGobi and XGvis programs for graphical data analysis.
@item zmatrix
Matrices with numeric indices starting at zero rather than one.
@end table

@noindent
See CRAN @file{src/contrib/INDEX} for more information.

There is also a CRAN @file{src/contrib/Devel} directory which contains
packages still ``under development'' or depending on features only
present in the current development versions of R.  Volunteers are
invited to give these a try, of course.  This area of CRAN currently
contains

@table @strong
@item HTML
Functions for exporting R objects as @HTML{} tables.
@item Java
An interface from R to Java to create and call Java objects and
methods.
@item RMySQL
An interface between R and the MySQL database system.
@item RPgSQL
An interface between R and the PostgreSQL database system.
@item Rstreams
Binary file stream support functions.
@item cmprsk
Estimation, testing and regression modeling of subdistribution functions
in competing risks.
@item cxx
A small C++ test package.
@item dopt
Finding D-optimal experimental designs.
@item dse
Multivariate time series.
@item exactDistr
Exact distributions for rank tests.
@item foreign
Functions for reading data stored by statistical software like Minitab,
SAS, SPSS, etc.
@item funfits
An integrated set of functions for fitting curves and surfaces including
thin plate splines, kriging and neural networks.
@item hdf5
Interface to the NCSA HDF5 library.
@item multidim
Code for correspondence analysis and other multidimensional descriptive
statistics.
@item netCDF
Read data from netCDF files.
@item syskern
Functions for writing code that is OS and R/S independent.
@item tframe
Functions for writing code that is independent of the representation of
time.
@item timeslab
Time series routines.
@end table

@email{jlindsey@@luc.ac.be, Jim Lindsey} has written a collection of R
packages for nonlinear regression and repeated measurements, consisting
of @strong{event} (event history procedures and models), @strong{gnlm}
(generalized nonlinear regression models), @strong{growth} (multivariate
normal and elliptically-contoured repeated measurements models),
@strong{repeated} (non-normal repeated measurements models),
@strong{rmutil} (utilities for nonlinear regression and repeated
measurements), and @strong{stable} (probability functions and
generalized regression models for stable distributions).  All analyses
in the new edition of his book ``Models for Repeated Measurements''
(1999, Oxford University Press) were carried out using these packages.
Jim has also started @strong{dna}, a package with procedures for the
analysis of DNA sequences.  Jim's packages can be obtained from
@uref{http://www.luc.ac.be/~jlindsey/rcode.html}.

@c @email{hfe@@math.uio.no, Harald Fekjaer} has written @strong{addreg}, a
@c package for additive hazards regression, which can be obtained from
@c @uref{http://www.med.uio.no/imb/stat/addreg/}.

More code has been posted to the r-help mailing list, and can be
obtained from the mailing list archive.

@node How can add-on packages be installed?, How can add-on packages be used?, Which add-on packages exist for R?, R Add-On Packages
@section How can add-on packages be installed?

(Unix only.)  The add-on packages on CRAN come as gzipped tar files
named @code{@var{pkg}_@var{version}.tar.gz}, which may in fact be
``bundles'' containing more than one package.  Provided that
@command{tar} and @command{gzip} are available on your system, type

@example
$ R INSTALL /path/to/@var{pkg}_@var{version}.tar.gz
@end example

@noindent
at the shell prompt to install to the default R directory tree (the
@file{library} subdirectory of @file{R_HOME}).  To install to another
tree (e.g., your private one), use

@example
$ R INSTALL -l @var{lib} /path/to/@var{pkg}_@var{version}.tar.gz
@end example

@noindent
where @var{lib} gives the path to the library tree to install to.

Even more conveniently, you can install and automatically update
packages from within R if you have access to CRAN.  See the help page
for @code{CRAN.packages()} for more information.

You can use several library trees of add-on packages.  The easiest way
to tell R to use these is via the environment variable @env{R_LIBS}
which should be a colon-separated list of directories at which R library
trees are rooted.  You do not have to specify the default tree in
@env{R_LIBS}.  E.g., to use a private tree in @file{$HOME/lib/R} and a
public site-wide tree in @file{/usr/local/lib/R-contrib}, put

@example
R_LIBS="$HOME/lib/R:/usr/local/lib/R-contrib"; export R_LIBS
@end example

@noindent
into your (Bourne) shell profile or your @file{~/.Renviron} file.

@node How can add-on packages be used?, How can add-on packages be removed?, How can add-on packages be installed?, R Add-On Packages
@section How can add-on packages be used?

To find out which additional packages are available on your system, type

@example
library()
@end example

@noindent
at the R prompt.  

This produces something like

@smallexample
Packages in `/home/me/lib/R':

mystuff      My own R functions, nicely packaged but not documented

Packages in `/usr/local/lib/R/library':

MASS         Main package of Venables and Ripley's MASS
base         The R base package
class        Functions for classification
cluster      Functions for clustering
ctest        Classical tests
date         Functions for handling dates
eda          Exploratory Data Analysis
gee          Generalized Estimating Equation models
locfit       Local regression, likelihood and density estimation
lqs          Resistant regression and covariance estimation
modreg       Modern regression: smoothing and local methods
mva          Classical Multivariate Analysis
nlme         Gaussian linear and nonlinear mixed-effects models
nls          Nonlinear regression
nnet         Software for feed-forward neural networks with a single
             hidden layer and for multinomial log-linear models.
splines      Regression spline functions and classes
stepfun      Step functions, including empirical distributions
survival5    Survival analysis
tcltk        Interface to Tcl/Tk
ts           Time series functions
@end smallexample

You can ``load'' the installed package @var{pkg} by

@example
library(@var{pkg})
@end example

You can then find out which functions it provides by typing one of

@example
help(package = @var{pkg})
library(help = @var{pkg})
@end example

You can unload the loaded package @var{pkg} by

@example
detach("package:@var{pkg}")
@end example

@node How can add-on packages be removed?, How can I create an R package?, How can add-on packages be used?, R Add-On Packages
@section How can add-on packages be removed?

To remove the packages @var{pkg_1}, @dots{}, @var{pkg_n} from the
default library or the library @var{lib}, do

@example
$ R REMOVE @var{pkg_1} @dots{} @var{pkg_n}
@end example

@noindent
or

@example
$ R REMOVE -l @var{lib} @var{pkg_1} @dots{} @var{pkg_n}
@end example

@noindent
respectively.

@node How can I create an R package?, How can I contribute to R?, How can add-on packages be removed?, R Add-On Packages
@section How can I create an R package?

A package consists of a subdirectory containing the files
@file{DESCRIPTION} and @file{INDEX}, and the subdirectories @file{R},
@file{data}, @file{exec}, @file{inst}, @file{man}, @file{src}, and
@file{tests} (some of which can be missing).  Optionally the package can
also contain script files @file{configure} and @file{cleanup} which are
executed before and after installation.

@ifclear UseExternalXrefs
See section ``Creating R packages'' in @cite{Writing R Extensions}, for
details.
@end ifclear
@ifset UseExternalXrefs
@xref{Creating R packages, , Creating R packages, R-exts, Writing R
Extensions}, for details.
@end ifset
This manual is included in the R distribution, @pxref{What documentation
exists for R?}, and gives information on package structure, the
configure and cleanup mechanisms, and on automated package checking and
building.

@xref{What is CRAN?}, for information on uploading a package to CRAN.

@node How can I contribute to R?,  , How can I create an R package?, R Add-On Packages
@section How can I contribute to R?

R is in active development and there is always a risk of bugs creeping
in.  Also, the developers do not have access to all possible machines
capable of running R.  So, simply using it and communicating problems is
certainly of great value.

One place where functionality is still missing is the modeling software
as described in ``Statistical Models in S'' (see @ref{What is S?});
Generalized Additive Models (@strong{gam}) and some of the nonlinear
modeling code are not there yet.

The @uref{http://developer.r-project.org/, R Developer Page} acts as an
intermediate repository for more or less finalized ideas and plans for
the R statistical system.  It contains (pointers to) TODO lists, RFCs,
various other writeups, ideas lists, and CVS miscellania.

Many (more) of the packages available at the Statlib S Repository might
be worth porting to R.

If you are interested in working on any of these projects, please notify
@email{Kurt.Hornik@@r-project.org, Kurt Hornik}.

@node R and Emacs, R Miscellania, R Add-On Packages, Top
@chapter R and Emacs

@menu
* Is there Emacs support for R?::  
* Should I run R from within Emacs?::  
* Debugging R from within Emacs::  
@end menu

@node Is there Emacs support for R?, Should I run R from within Emacs?, R and Emacs, R and Emacs
@section Is there Emacs support for R?

There is an Emacs package called ESS (``Emacs Speaks Statistics'') which
provides a standard interface between statistical programs and
statistical processes.  It is intended to provide assistance for
interactive statistical programming and data analysis.  Languages
supported include: S dialects (S 3/4, @SPLUS{} 3.x/4.x/5.x, and R),
LispStat dialects (XLispStat, ViSta), SAS, Stata, SPSS dialects (SPSS,
PSPP) and SCA.

ESS grew out of the need for bug fixes and extensions to S-mode 4.8
(which was a GNU Emacs interface to S/@SPLUS{} version 3 only).  The
current set of developers desired support for XEmacs, R, S4, and MS
Windows.  In addition, with new modes being developed for R, Stata, and
SAS, it was felt that a unifying interface and framework for the user
interface would benefit both the user and the developer, by helping both
groups conform to standard Emacs usage.  The end result is an increase
in efficiency for statistical programming and data analysis, over the
usual tools.

R support contains code for editing R source code (syntactic indentation
and highlighting of source code, partial evaluations of code, loading
and error-checking of code, and source code revision maintenance) and
documentation (syntactic indentation and highlighting of source code,
sending examples to running ESS process, and previewing), interacting
with an inferior R process from within Emacs (command-line editing,
searchable command history, command-line completion of R object and file
names, quick access to object and search lists, transcript recording,
and an interface to the help system), and transcript manipulation
(recording and saving transcript files, manipulating and editing saved
transcripts, and re-evaluating commands from transcript files).

The latest versions of ESS are available from
@uref{http://ess.stat.wisc.edu/pub/ESS/} or
@uref{ftp://ess.stat.wisc.edu/pub/ESS/}, or via CRAN.  The @HTML{}
version of the documentation can be found at
@uref{http://stat.ethz.ch/ESS/}.

ESS comes with detailed installation instructions.

For help with ESS, send email to @email{ESS-help@@stat.ethz.ch}.

Please send bug reports and suggestions on ESS to
@email{ESS-bugs@@stat.math.ethz.ch}.  The easiest way to do this from is
within Emacs by typing @kbd{M-x ess-submit-bug-report} or using the
[ESS] or [iESS] pulldown menus.

@node Should I run R from within Emacs?, Debugging R from within Emacs, Is there Emacs support for R?, R and Emacs
@section Should I run R from within Emacs?

Yes, @emph{definitely}.  Inferior R mode provides a readline/history
mechanism, object name completion, and syntax-based highlighting of the
interaction buffer using Font Lock mode, as well as a very convenient
interface to the R help system.

Of course, it also integrates nicely with the mechanisms for editing R
source using Emacs.  One can write code in one Emacs buffer and send
whole or parts of it for execution to R; this is helpful for both data
analysis and programming.  One can also seamlessly integrate with a
revision control system, in order to maintain a log of changes in your
programs and data, as well as to allow for the retrieval of past
versions of the code.

In addition, it allows you to keep a record of your session, which can
also be used for error recovery through the use of the transcript mode.

To specify command line arguments for the inferior R process, use
@kbd{C-u M-x R} for starting R.  This prompts you for the arguments; in
particular, you can increase the memory size this way (@pxref{Why does R
run out of memory?}).

@node Debugging R from within Emacs,  , Should I run R from within Emacs?, R and Emacs
@section Debugging R from within Emacs

To debug R ``from within Emacs'', there are several possibilities.  To
use the Emacs GUD (Grand Unified Debugger) library with the recommended
debugger GDB, type @kbd{M-x gdb} and give the path to the R
@emph{binary} as argument.  At the gdb prompt, set @env{R_HOME} and
other environment variables as needed (using e.g.@: @kbd{set env R_HOME
/path/to/R/}, but see also below), and start the binary with the desired
arguments (e.g., @kbd{run --vsize=12M}).

If you have ESS, you can do @kbd{C-u M-x R @key{RET} - d @key{SPC} g d b
@key{RET}} to start an inferior R process with arguments @option{-d
gdb}.

A third option is to start an inferior R process via ESS (@kbd{M-x R})
and then start GUD (@kbd{M-x gdb}) giving the R binary (using its full
path name) as the program to debug.  Use the program @command{ps} to
find the process number of the currently running R process then use the
@code{attach} command in gdb to attach it to that process.  One
advantage of this method is that you have separate @code{*R*} and
@code{*gud-gdb*} windows.  Within the @code{*R*} window you have all the
ESS facilities, such as object-name completion, that we know and love.

When using GUD mode for debugging from within Emacs, you may find it
most convenient to use the directory with your code in it as the current
working directory and then make a symbolic link from that directory to
the R binary.  That way @file{.gdbinit} can stay in the directory with
the code and be used to set up the environment and the search paths for
the source, e.g.@: as follows:

@smallexample
set env R_HOME /opt/R
set env R_PAPERSIZE letter
set env R_PRINTCMD lpr
dir /opt/R/src/appl
dir /opt/R/src/main
dir /opt/R/src/nmath
dir /opt/R/src/unix
@end smallexample

@node R Miscellania, R Programming, R and Emacs, Top
@chapter R Miscellania

@menu
* Why does R run out of memory?::  
* Why does sourcing a correct file fail?::  
* How can I set components of a list to NULL?::  
* How can I save my workspace?::  
* How can I clean up my workspace?::  
* How can I get eval() and D() to work?::  
* Why do my matrices lose dimensions?::  
* How does autoloading work?::  
* How should I set options?::   
* How do file names work in Windows?::  
* Why does plotting give a color allocation error?::  
* Is R Y2K-compliant?::         
* How do I convert factors to numeric?::  
* Are Trellis displays implemented in R?::  
* What are the enclosing and parent environments?::  
@end menu

@node Why does R run out of memory?, Why does sourcing a correct file fail?, R Miscellania, R Miscellania
@section Why does R run out of memory?

R (currently) uses a @emph{static} memory model.  This means that when
it starts up, it asks the operating system to reserve a fixed amount of
memory for it.  The size of this chunk cannot be changed subsequently.
Hence, it can happen that not enough memory was allocated, e.g., when
trying to read large data sets into R.

In these cases, you should restart R with more memory available, using
the command line options @option{--nsize} and @option{--vsize}.  To
understand these options, one needs to know that R maintains separate
areas for fixed and variable sized objects.  The first of these is
allocated as an array of ``cons cells'' (Lisp programmers will know what
they are, others may think of them as the building blocks of the
language itself, parse trees, etc.), and the second are thrown on a
``heap''.  The @option{--nsize} option can be used to specify the number
of cons cells which R is to use (the default is 250000), and the
@option{--vsize} option to specify the size of the vector heap in bytes
(the default is @w{6 MB}).  Both options must either be integers or
integers ending with @samp{M}, @samp{K}, or @samp{k} meaning `Mega'
(2^20), (computer) `Kilo' (2^10), or regular `kilo' (1000).

E.g., to read in a table of 5000 observations on 40 numeric variables,
@samp{R --vsize=6M} should do (which currently is the default).

Note that the information on where to find vectors and strings on the
heap is stored using cons cells.  Thus, it may also be necessary to
allocate more space for cons cells in order to perform computations with
very ``large'' variable-size objects.

You can find out the current memory consumption (the proportion of heap
and cons cells used) by typing @kbd{gc()} at the R prompt.  This may
help you in finding out whether to increase @option{--vsize} or
@option{--nsize}.  Note that following @kbd{gcinfo(TRUE)}, automatic
garbage collection always prints memory use statistics.

As of version 0.62.3, R will tell you whether you ran out of cons or
heap memory.

The defaults for @option{--nsize} and @option{--vsize} can be changed by
setting the environment variables @env{R_NSIZE} and @env{R_VSIZE}
respectively, perhaps most conveniently on Unix in the R environment
file (@file{~/.Renviron} by default).

When using @code{read.table()}, the memory requirements are in fact
higher than anticipated, because the file is first read in as one long
string which is then split again.  Use @code{scan()} if possible in
case you run out of memory when reading in a large table.

R version 1.2.0 introduces a new ``generational'' garbage collector,
which will increase the memory available to R as needed.  The interface
for controlling the generational collector is still experimental and in
flux.  Currently, the command line arguments and environment variables
described above can be used to supply minimal values for the sizes of
the node and vector heaps.

@node Why does sourcing a correct file fail?, How can I set components of a list to NULL?, Why does R run out of memory?, R Miscellania
@section Why does sourcing a correct file fail?

R sometimes has problems parsing a file which does not end in a newline.
This can happen for example when Emacs is used for editing the file and
@code{next-line-add-newlines} is set to @code{nil}.  To avoid the
problem, either set @code{require-final-newline} to a non-@code{nil}
value in one of your Emacs startup files, or make sure R-mode (@pxref{Is
there Emacs support for R?}) is used for editing R source files (which
locally ensures this setting).

Earlier R versions had a similar problem when reading in data files, but
this should have been taken care of now.

@node How can I set components of a list to NULL?, How can I save my workspace?, Why does sourcing a correct file fail?, R Miscellania
@section How can I set components of a list to NULL?

You can use

@example
x[i] <- list(NULL)
@end example

@noindent
to set component @code{i} of the list @code{x} to @code{NULL}, similarly
for named components.  Do not set @code{x[i]} or @code{x[[i]]} to
@code{NULL}, because this will remove the corresponding component from
the list.

For dropping the row names of a matrix @code{x}, it may be easier to use
@code{rownames(x) <- NULL}, similarly for column names.

@node How can I save my workspace?, How can I clean up my workspace?, How can I set components of a list to NULL?, R Miscellania
@section How can I save my workspace?

@code{save.image()} saves the objects in the user's @code{.GlobalEnv} to
the file @file{.RData} in the R startup directory.  (This is also what
happens after @kbd{q("yes")}.)  Using @code{save.image(@var{file})} one
can save the image under a different name.

@node How can I clean up my workspace?, How can I get eval() and D() to work?, How can I save my workspace?, R Miscellania
@section How can I clean up my workspace?

To remove all objects in the currently active environment (typically
@code{.GlobalEnv}), you can do

@example
rm(list = ls(all = TRUE))
@end example

@noindent
(Without @option{all = TRUE}, only the objects with names not starting
with a @samp{.} are removed.)

@node How can I get eval() and D() to work?, Why do my matrices lose dimensions?, How can I clean up my workspace?, R Miscellania
@section How can I get eval() and D() to work?

Strange things will happen if you use @code{eval(print(x), envir = e)}
or @code{D(x^2, "x")}.  The first one will either tell you that
"@code{x}" is not found, or print the value of the wrong @code{x}.
The other one will likely return zero if @code{x} exists, and an error
otherwise.

This is because in both cases, the first argument is evaluated in the
calling environment first.  The result (which should be an object of
mode @code{"expression"} or @code{"call"}) is then evaluated or
differentiated.  What you (most likely) really want is obtained by
``quoting'' the first argument upon surrounding it with
@code{expression()}.  For example,

@example
R> D(expression(x^2), "x")
2 * x
@end example

Although this behavior may initially seem to be rather strange, is
perfectly logical.  The ``intuitive'' behavior could easily be
implemented, but problems would arise whenever the expression is
contained in a variable, passed as a parameter, or is the result of a
function call.  Consider for instance the semantics in cases like

@example
D2 <- function(e, n) D(D(e, n), n)
@end example

@noindent
or

@example
g <- function(y) eval(substitute(y), sys.frame(sys.parent(n = 2)))
g(a * b)
@end example

See the help page for @code{deriv()} for more examples.

@node Why do my matrices lose dimensions?, How does autoloading work?, How can I get eval() and D() to work?, R Miscellania
@section Why do my matrices lose dimensions?

When a matrix with a single row or column is created by a subscripting
operation, e.g., @code{row <- mat[2, ]}, it is by default turned into a
vector.  In a similar way if an array with dimension, say, @w{2 x 3 x 1
x 4} is created by subscripting it will be coerced into a @w{2 x 3 x 4}
array, losing the unnecessary dimension.  After much discussion this has
been determined to be a @emph{feature}.

To prevent this happening, add the option @option{drop = FALSE} to the
subscripting.  For example,

@example
rowmatrix <- mat[2, , drop = FALSE]  # @r{creates a row matrix}
colmatrix <- mat[, 2, drop = FALSE]  # @r{creates a column matrix}
a <- b[1, 1, 1, drop = FALSE]        # @r{creates a 1 x 1 x 1 array}
@end example

The @option{drop = FALSE} option should be used defensively when
programming.  For example, the statement

@example
somerows <- mat[index, ]
@end example

@noindent
will return a vector rather than a matrix if @code{index} happens to
have length 1, causing errors later in the code.  It should probably be
rewritten as

@example
somerows <- mat[index, , drop = FALSE]
@end example

@node How does autoloading work?, How should I set options?, Why do my matrices lose dimensions?, R Miscellania
@section How does autoloading work?

R has a special environment called @code{.AutoloadEnv}.  Using
@kbd{autoload(@var{name}, @var{pkg})}, where @var{name} and
@var{pkg} are strings giving the names of an object and the package
containing it, stores some information in this environment.  When R
tries to evaluate @var{name}, it loads the corresponding package
@var{pkg} and reevaluates @var{name} in the new package's
environment.

Using this mechanism makes R behave as if the package was loaded, but
does not occupy memory (yet).

See the help page for @code{autoload()} for a very nice example.

@node How should I set options?, How do file names work in Windows?, How does autoloading work?, R Miscellania
@section How should I set options?

The function @code{options()} allows setting and examining a variety of
global ``options'' which affect the way in which R computes and displays
its results.  The variable @code{.Options} holds the current values of
these options, but should never directly be assigned to unless you want
to drive yourself crazy---simply pretend that it is a ``read-only''
variable.

For example, given

@example
test1 <- function(x = pi, dig = 3) @{
  oo <- options(digits = dig); on.exit(options(oo));
  cat(.Options$digits, x, "\n")
@}
test2 <- function(x = pi, dig = 3) @{
  .Options$digits <- dig
  cat(.Options$digits, x, "\n")
@}
@end example

@noindent
we obtain:

@example
R> test1()
3 3.14 
R> test2()
3 3.141593
@end example

What is really used is the @emph{global} value of @code{.Options}, and
using @kbd{options(OPT = VAL)} correctly updates it.  Local copies of
@code{.Options}, either in @code{.GlobalEnv} or in a function
environment (frame), are just silently disregarded.

@node How do file names work in Windows?, Why does plotting give a color allocation error?, How should I set options?, R Miscellania
@section How do file names work in Windows?

As R uses C-style string handling, @samp{\} is treated as an escape
character, so that for example one can enter a newline as @samp{\n}.
When you really need a @samp{\}, you have to escape it with another
@samp{\}.

Thus, in filenames use something like @code{"c:\\data\\money.dat"}.  You
can also replace @samp{\} by @samp{/} (@code{"c:/data/money.dat"}).

@node Why does plotting give a color allocation error?, Is R Y2K-compliant?, How do file names work in Windows?, R Miscellania
@section Why does plotting give a color allocation error?

Sometimes plotting, e.g., when running @code{demo(image)}, results in
``Error: color allocation error''.  This is an X problem, and only
indirectly related to R.  It occurs when applications started prior to R
have used all the available colors.  (How many colors are available
depends on the X configuration; sometimes only 256 colors can be used.)

One application which is notorious for ``eating'' colors is Netscape.
If the problem occurs when Netscape is running, try (re)starting it with
either the @option{-no-install} (to use the default colormap) or the
@option{-install} (to install a private colormap) option.

You could also set the @code{colortype} of @code{X11()} to
@code{"pseudo.cube"} rather than the default @code{"pseudo"}.  See the
help page for @code{X11()} for more information.

@node Is R Y2K-compliant?, How do I convert factors to numeric?, Why does plotting give a color allocation error?, R Miscellania
@section Is R Y2K-compliant?

We expect R to be Y2K compliant when compiled and run on a Y2K compliant
system.  In particular R does not internally represent or manipulate
dates as two-digit quantities.  However, no guarantee of Y2K compliance
is provided for R.  R is free software and comes with @emph{no warranty
whatsoever}.

R, like any other programming language, can be used to write programs
and manipulate data in ways that are not Y2K compliant.

@node How do I convert factors to numeric?, Are Trellis displays implemented in R?, Is R Y2K-compliant?, R Miscellania
@section How do I convert factors to numeric?

It may happen that when reading numeric data into R (usually, when
reading in a file), they come in as factors.  If @code{f} is such a
factor object, you can use

@example
as.numeric(as.character(f))
@end example

@noindent
to get the numbers back.  More efficient, but harder to remember, is

@example
as.numeric(levels(f))[as.integer(f)]
@end example

In any case, do not call @code{as.numeric()} or their likes directly.

@node Are Trellis displays implemented in R?, What are the enclosing and parent environments?, How do I convert factors to numeric?, R Miscellania
@section Are Trellis displays implemented in R?

Not yet.  Meanwhile, you could look at @code{coplot()} and
@code{dotplot()} which might do at least some of what you want.  Note
also that the R version of @code{pairs()} is fairly general and provides
most of the functionality of @code{splom()}, and that R's default plot
method has an argument @code{asp} allowing to specify (and fix against
device resizing) the aspect ratio of the plot.

(By the way, ``Trellis'' is a trademark which cannot be used in R;
instead, the term ``lattice'' has been proposed for the R equivalent.)

@node What are the enclosing and parent environments?,  , Are Trellis displays implemented in R?, R Miscellania
@section What are the enclosing and parent environments?

Inside a function you may want to access variables in two additional
environments: the one that the function was defined in (``enclosing''),
and the one it was invoked in (``parent'')

If you create a function at the command line or load it in a package its
enclosing environment is the global workspace.  If you define a function
@code{f()} inside another function @code{g()} its enclosing environment
is the environment inside @code{g()}.  The enclosing environment for a
function is fixed when the function is created.  You can find out the
enclosing environment for a function @code{f()} using
@code{environment(f)}.

The ``parent'' environment, on the other hand, is defined when you
invoke a function.  If you invoke @code{lm()} at the command line its
parent environment is the global workspace, if you invoke it inside a
function @code{f()} then its parent environment is the environment
inside @code{f()}.  You can find out the parent environment for an
invocation of a function by using @code{parent.frame()} or
@code{sys.frame(sys.parent())}.

So for most user-visible functions the enclosing environment will be the
global workspace, since that is where most functions are defined.  The
parent environment will be wherever the function happens to be called
from.  If a function @code{f()} is defined inside another function
@code{g()} it will probably be used inside @code{g()} as well, so its
parent environment and enclosing environment will probably be the same.

Parent environments are important because things like model formulas
need to be evaluated in the environment the function was called from,
since that's where all the variables will be available.  This relies on
the parent environment being potentially different with each invocation.

Enclosing environments are important because a function can use
variables in the enclosing environment to share information with other
functions or with other invocations of itself (see the section on
lexical scoping).  This relies on the enclosing environment being the
same each time the function is invoked.

Scoping @emph{is} hard.  Looking at examples helps.  It is particularly
instructive to look at examples that work differently in R and S and try
to see why they differ.  One way to describe the scoping differences
between R and S is to say that in S the enclosing environment is
@emph{always} the global workspace, but in R the enclosing environment
is wherever the function was created.

@node R Programming, R Bugs, R Miscellania, Top
@chapter R Programming

@menu
* How should I write summary methods?::  
* How can I debug dynamically loaded code?::  
* How can I inspect R objects when debugging?::  
@end menu

@node How should I write summary methods?, How can I debug dynamically loaded code?, R Programming, R Programming
@section How should I write summary methods?

Suppose you want to provide a summary method for class @code{foo}.  Then
@code{summary.foo()} should not print anything, but return an object of
class @code{"summary.foo"}, @emph{and} you should write a method
@code{print.summary.foo()} which nicely prints the summary information
and invisibly returns its object.  This approach is preferred over
having @code{summary.foo()} print summary information and return
something useful, as sometimes you need to grab something computed by
@code{summary()} inside a function or similar.  In such cases you don't
want anything printed.

@node How can I debug dynamically loaded code?, How can I inspect R objects when debugging?, How should I write summary methods?, R Programming
@section How can I debug dynamically loaded code?

Roughly speaking, you need to start R inside the debugger, load the
code, send an interrupt, and then set the required breakpoints.

@ifclear UseExternalXrefs
See section ``Finding entry points in dynamically loaded code'' in
@cite{Writing R Extensions}.
@end ifclear
@ifset UseExternalXrefs
@xref{Finding entry points, , Finding entry points in dynamically loaded
code, R-exts, Writing R Extensions}.
@end ifset
This manual is included in the R distribution, @pxref{What documentation
exists for R?}.

@node How can I inspect R objects when debugging?,  , How can I debug dynamically loaded code?, R Programming
@section How can I inspect R objects when debugging?

The most convenient way is to call @code{R_PV} from the symbolic
debugger.

@ifclear UseExternalXrefs
See section ``Inspecting R objects when debugging'' in @cite{Writing R
Extensions}.
@end ifclear
@ifset UseExternalXrefs
@xref{Inspecting R objects, , Inspecting R objects when debugging,
R-exts, Writing R Extensions}.
@end ifset

@node R Bugs, Acknowledgments, R Programming, Top
@chapter R Bugs

@menu
* What is a bug?::              
* How to report a bug::         
@end menu

@node What is a bug?, How to report a bug, R Bugs, R Bugs
@section What is a bug?

If R executes an illegal instruction, or dies with an operating system
error message that indicates a problem in the program (as opposed to
something like ``disk full''), then it is certainly a bug.  If you call
@code{.C()}, @code{.Fortran()}, @code{.External()} or @code{.Call()} (or
@code{.Internal()}) yourself (or in a function you wrote), you can
always crash R by using wrong argument types (modes).  This is not a
bug.

Taking forever to complete a command can be a bug, but you must make
certain that it was really R's fault.  Some commands simply take a long
time.  If the input was such that you @emph{know} it should have been
processed quickly, report a bug.  If you don't know whether the command
should take a long time, find out by looking in the manual or by asking
for assistance.

If a command you are familiar with causes an R error message in a case
where its usual definition ought to be reasonable, it is probably a bug.
If a command does the wrong thing, that is a bug.  But be sure you know
for certain what it ought to have done.  If you aren't familiar with the
command, or don't know for certain how the command is supposed to work,
then it might actually be working right.  Rather than jumping to
conclusions, show the problem to someone who knows for certain.

Finally, a command's intended definition may not be best for statistical
analysis.  This is a very important sort of problem, but it is also a
matter of judgment.  Also, it is easy to come to such a conclusion out
of ignorance of some of the existing features.  It is probably best not
to complain about such a problem until you have checked the
documentation in the usual ways, feel confident that you understand it,
and know for certain that what you want is not available.  If you are
not sure what the command is supposed to do after a careful reading of
the manual this indicates a bug in the manual.  The manual's job is to
make everything clear.  It is just as important to report documentation
bugs as program bugs.  However, we know that the introductory
documentation is seriously inadequate, so you don't need to report this.

If the online argument list of a function disagrees with the manual, one
of them must be wrong, so report the bug.

@node How to report a bug,  , What is a bug?, R Bugs
@section How to report a bug

When you decide that there is a bug, it is important to report it and to
report it in a way which is useful.  What is most useful is an exact
description of what commands you type, starting with the shell command
to run R, until the problem happens.  Always include the version of R,
machine, and operating system that you are using; type @kbd{version} in
R to print this.

The most important principle in reporting a bug is to report
@emph{facts}, not hypotheses or categorizations.  It is always easier to
report the facts, but people seem to prefer to strain to posit
explanations and report them instead.  If the explanations are based on
guesses about how R is implemented, they will be useless; others will
have to try to figure out what the facts must have been to lead to such
speculations.  Sometimes this is impossible.  But in any case, it is
unnecessary work for the ones trying to fix the problem.

For example, suppose that on a data set which you know to be quite large
the command

@smallexample
R> data.frame(x, y, z, monday, tuesday)
@end smallexample

@noindent
never returns.  Do not report that @code{data.frame()} fails for large
data sets.  Perhaps it fails when a variable name is a day of the week.
If this is so then when others got your report they would try out the
@code{data.frame()} command on a large data set, probably with no day of
the week variable name, and not see any problem.  There is no way in the
world that others could guess that they should try a day of the week
variable name.

Or perhaps the command fails because the last command you used was a
method for @code{"["()} that had a bug causing R's internal data
structures to be corrupted and making the @code{data.frame()} command
fail from then on.  This is why others need to know what other commands
you have typed (or read from your startup file).

It is very useful to try and find simple examples that produce
apparently the same bug, and somewhat useful to find simple examples
that might be expected to produce the bug but actually do not.  If you
want to debug the problem and find exactly what caused it, that is
wonderful.  You should still report the facts as well as any
explanations or solutions.  Please include an example that reproduces
the problem, preferably the simplest one you have found.

Invoking R with the @option{--vanilla} option may help in isolating a
bug.  This ensures that the site profile and saved data files are not
read.

On Unix systems a bug report can be generated using the function
@code{bug.report()}.  This automatically includes the version
information and sends the bug to the correct address.  Alternatively the
bug report can be emailed to @email{r-bugs@@lists.r-project.org} or
submitted to the Web page at @uref{http://bugs.r-project.org/}.

Bug reports on contributed packages should perhaps be sent to the
package maintainer rather than to r-bugs.

@node Acknowledgments,  , R Bugs, Top
@chapter Acknowledgments

Of course, many many thanks to Robert and Ross for the R system, and to
the package writers and porters for adding to it.

Special thanks go to Doug Bates, Peter Dalgaard, Paul Gilbert, Fritz
Leisch, Jim Lindsey, Thomas Lumley, Martin Maechler, Brian D. Ripley,
Anthony Rossini, and Andreas Weingessel for their comments which helped
me improve this FAQ.

More to some soon @dots{}

@bye

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